Hassell's original type-III response (assuming replacement)

Usage

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Arguments

data

A data frame containing X and Y (at least).

samp

A vector specifying the rows of data to use in the fit. Provided by boot() or manually, as required.

start

A named list. Starting values for items to be optimised. Usually b, c and h.

fixed

A names list. 'Fixed data' (not optimised). Usually T.

boot

A logical. Is the function being called for use by boot()?

windows

A logical. Is the operating system Microsoft Windows?

b,c,h

Hassel's b and c, plus h, the handling time. Usually items to be optimised.

T

T, the total time available.

X

The X variable. Usually prey density.

Y

The Y variable. Usually the number of prey consumed.

Details

This implements the original Hassel's type-III functional response, assuming prey density is kept constant (i.e. a 'replacement' experimental design). In practice, constant prey density might be an unrealistic assumption, in which case users should consider the hassIIIr function instead.

In Hassel et al.'s original formulation, the capture rate a is assumed to vary with the prey density in the following hyperbolic relationship:

a <- (b*X)/(1+c*X)

where b and c are coefficients to be fitted and X is the initial prey density. This is the initial formulation of Hassell et al. (1977) and uses their naming conventions. The value for a is then used within a traditional Holling's disc equation (see hollingsII).

None of these functions are designed to be called directly, though they are all exported so that the user can do so if desired. The intention is that they are called via frair_fit, which calls them in the order they are specified above.

hassIII_fit does the heavy lifting and also pulls double duty as the statistic function for bootstrapping (viaboot() in the boot package). The windows argument if required to prevent needless calls to require(frair) on platforms that can manage sane parallel processing.

The core fitting is done by mle2 from the bbmle package and users are directed there for more information. mle2 uses the hassIII_nll function to optimise hassIII.

Further references and recommended reading can be found on the help page for frair_fit.